Tunix Gemma Reinforcement Learning

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Authors

Affiliation

CJ Jones

Georgetown University

Adam Stein

Georgetown University

1 Abstract

2 Introduction

3 Literature Review

4 Dataset

Fig. 1: Annotated chess board output from ChessReD Dataset

Fig. 2: Chess board with no bounding boxes output from ChessReD Dataset

5 Methods

5.1 Convolutional Feature Extractor

Fig. 3: End-to-end architecture for combined chessboard recognition network

5.2 Training Procedure

Component	# Params	Trainable @ Start?	Trainable @ End
ConvNeXt-B backbone	88 M	❌ frozen	✅ last 3 / 12
Transformer (4 layers)	17 M	❌ frozen	✅ last 1 / 4
Square tokens	64 × 1024 ≈ 66 k	✅	✅
Linear head	13 k	✅	✅
Total	105 M	79 k (0.07%)	~30 M (28.6%)

Fig 4. Summary of model parameter counts, training visibility at start and end of training, and staged unfreezing schedule.

6 Results

6.1 Error Distribution and Exact-Match Accuracy

Metric	Baseline ResNeXt (2023)	ConvNeXt-TE (+Tx) (Ours)
Mean incorrect squares / board	3.40	4.33
Boards with no mistakes (%)	15.26	9.12
Boards with ≤1 mistake (%)	25.92	19.38
Per-square error rate (%)	5.31	5.94

Fig. 5: Distribution of incorrect squares per predicted board. The model most frequently makes 2–4 errors per board, though outlier examples with 10+ mistakes persist.

6.2 6.2 Confusions

6.3 6.3 Qualitative

Fig. 7: Example of model prediction on a validation board. From left to right: original image, ground-truth board matrix, model-predicted matrix. The predicted board achieves a per-square accuracy of 0.984.

6.4 6.4 Training Dynamics

Fig. 8: Training and validation loss curves across all epochs. Smooth convergence reflects the stability of the training setup and the effectiveness of progressive unfreezing.

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7 Discussion

8 References

Masouris, Athanasios, and Jan C. van Gemert. End-to-End Chess Recognition. Delft: Delft University of Technology, 2023. https://github.com/ThanosM97/end-to-end-chess-recognition.